Realistic Differentially-Private Transmission Power Flow Data Release

• URL: http://arxiv.org/abs/2103.14036v1
• Date: Thu, 25 Mar 2021 04:04:12 GMT
• Title: Realistic Differentially-Private Transmission Power Flow Data Release
• Authors: David Smith, Frederik Geth, Elliott Vercoe, Andrew Feutrill, Ming Ding, Jonathan Chan, James Foster and Thierry Rakotoarivelo
• Abstract summary: We propose a fundamentally different post-processing method, using public information of grid losses rather than power dispatch. We protect more sensitive parameters, i.e., branch shuntance in addition to series impedance. Our approach addresses a more feasible and realistic scenario, and provides higher than state-of-the-art privacy guarantees.
• Score: 12.425053979364362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers. This critical challenge has recently been somewhat addressed in [1]. This paper significantly extends this existing work. First, we reduce the potential leakage information by proposing a fundamentally different post-processing method, using public information of grid losses rather than power dispatch, which achieve a higher level of privacy protection. Second, we protect more sensitive parameters, i.e., branch shunt susceptance in addition to series impedance (complete pi-model). This protects power flow data for the transmission high-voltage networks, using differentially private transformations that maintain the optimal power flow consistent with, and faithful to, expected model behaviour. Third, we tested our approach at a larger scale than previous work, using the PGLib-OPF test cases [10]. This resulted in the successful obfuscation of up to a 4700-bus system, which can be successfully solved with faithfulness of parameters and good utility to data analysts. Our approach addresses a more feasible and realistic scenario, and provides higher than state-of-the-art privacy guarantees, while maintaining solvability, fidelity and feasibility of the system.

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